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Record W3199602014 · doi:10.1108/ijmhsc-03-2021-0023

Life after trafficking: reintegration experiences of human trafficking survivors in Nigeria

2021· article· en· W3199602014 on OpenAlexafffund
Uwafiokun Idemudia, Nnenna Okoli, Mary Goitom, Sylvia Bawa

Bibliographic record

VenueInternational Journal of Migration Health and Social Care · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsThematic analysisContext (archaeology)Human traffickingQualitative researchGrounded theoryDemobilizationPsychologyCriminologyPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

Purpose Reintegration programs have emerged as part of the regimes of care for survivors of human trafficking. However, empirical analysis of the reintegration outcomes for survivors remains limited in the African context. Hence, this paper aims to examine the challenges and opportunities of reintegration assistance programs for survivors of human trafficking in Nigeria. Design/methodology/approach Drawing on qualitative methods of data collection and analysis, this study conducted semi-structured interviews with repatriated women who have accessed reintegration assistance in Nigeria, and data was analyzed using thematic analysis. Findings The findings suggest that while the reintegration programs might address the procedural aspect of reintegration, the achievement of substantive reintegration remains incomplete. This is because of the structural conditions of the context within which reintegration is supposed to occur. Practical implications There is a need to take seriously the distinction between the reintegration of survivors into a new community or a former community in the design of a regime of care for survivors of human trafficking in Africa. Crucially, the focus on procedural reintegration should not also divert attention away from the structural conditions and reforms needed to ensure survivors achieve substantive reintegration. Originality/value This paper contributes to the limited literature on life after trafficking and demonstrates the strengths and limitations of reintegration programs as a regime of care for survivors of human trafficking. In addition, this study empirically grounded the theoretical distinction between different aspects of the process of reintegration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.358
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2021
Admission routes2
Has abstractyes

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